Perspectives on Gender in Science, Technology, and Innovation: A Review of Sub-Saharan Africa's Science Granting Councils and Achieving the Sustainable Development Goals
Bibliographic record
Abstract
Africa's focus on science, technology, and innovation (STI) has grown over the last decade, with emerging examples of good practice. There are however numerous challenges to sustainable development in Africa; for example, inequalities within and among African countries are rising and enormous disparities of opportunity, wealth, and power persist. While policy makers and organizations have put increasing emphasis on integrating gender into STI policies and initiatives as a means to achieve gender equality for all women and girls, inequality remains a key challenge to continental sustainable development. STI funders such as the Science Granting Councils (SGCs) in Africa are key players in national innovation systems. They advise and facilitate policy and program development, disburse funds, build research capacity, set and monitor research agendas, manage bilateral and multilateral STI agreements, and assess the communication, uptake, and impact of research. They, therefore, have a major role to play in enabling countries to achieve SDG5. This study assessed the current actions in gender mainstreaming across the SGCs and the status of gender research and collaboration in participating countries. Our findings provide evidence of uneven progress in promoting gender equality in the operations of the SGCs, including funding research and promoting the integration of gender dimensions in research content and curricula. All SGCs emphasized national commitments to gender, and the importance of gender in STI, but acknowledged that at the structural and institutional levels there was a misalignment between policy and practice. As expected, more men than women were employed across most levels at the SGCs and held positions of seniority and decision making. Most of the SGCs had very limited or no gender-related funding programs to promote gender and STI or to eliminate the barriers that women scholars face. This resulted in persistent inequalities in who received funding, the size of the grants they received, and in the knowledge production, collaboration, and the impact on their country's gender-related research. These findings suggest that SGCs need to strengthen their actions to mainstream gender if they are to achieve success with SDG5.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".